Search Results for author: Zhizhi Yu

Found 6 papers, 1 papers with code

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

no code implementations22 Feb 2023 Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu

Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.

TeKo: Text-Rich Graph Neural Networks with External Knowledge

no code implementations15 Jun 2022 Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu

Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i. e., networks).

Universal Graph Convolutional Networks

1 code implementation NeurIPS 2021 Di Jin, Zhizhi Yu, Cuiying Huo, Rui Wang, Xiao Wang, Dongxiao He, Jiawei Han

So can we reasonably utilize these segmentation rules to design a universal propagation mechanism independent of the network structural assumption?

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

no code implementations3 Jan 2021 Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang

We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Community Detection

BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks

no code implementations23 Oct 2020 Di Jin, Xiangchen Song, Zhizhi Yu, Ziyang Liu, Heling Zhang, Zhaomeng Cheng, Jiawei Han

We propose BiTe-GCN, a novel GCN architecture with bidirectional convolution of both topology and features on text-rich networks to solve these limitations.

GCN for HIN via Implicit Utilization of Attention and Meta-paths

no code implementations6 Jul 2020 Di Jin, Zhizhi Yu, Dongxiao He, Carl Yang, Philip S. Yu, Jiawei Han

Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.

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